Modeling, Learning, and Decision-Making

2024 Fall
#29010
COMPSCI 189 102 - DIS 102 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
06:00 pm - 06:59 pm
Wheeler 202
Class #:29010
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 102 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#29009
COMPSCI 189 101 - DIS 101 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
12:00 pm - 12:59 pm
Cory 540AB
Class #:29009
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
COMPSCI 189 - DIS 101 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#28867
COMPSCI 189 999 - DIS 999 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
12:00 am
Class #:28867
Units:4

Instruction Mode: In-Person Instruction

Open Seats

6 Unreserved Seats

COMPSCI 189 - DIS 999 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#28808
COMPSCI 189 110 - DIS 110 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
Th
10:00 am - 10:59 am
Wheeler 202
Class #:28808
Units:4

Instruction Mode: In-Person Instruction

Open Seats

39 Unreserved Seats

COMPSCI 189 - DIS 110 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#28752
COMPSCI 189 109 - DIS 109 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
07:00 pm - 07:59 pm
Cory 540AB
Class #:28752
Units:4

Instruction Mode: In-Person Instruction

Open Seats

36 Unreserved Seats

COMPSCI 189 - DIS 109 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#28486
COMPSCI 189 108 - DIS 108 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Aug 28, 2024 - Dec 13, 2024
We
01:00 pm - 01:59 pm
Wheeler 20
Class #:28486
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 108 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
2024 Fall
#28453
COMPSCI 189 001 - LEC 001 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jennifer Listgarten, Saeed Saremi
Aug 28, 2024 - Dec 13, 2024
Tu, Th
02:00 pm - 03:29 pm
HAAS Faculty Wing F295
Class #:28453
Units:4

Instruction Mode: In-Person Instruction

Open Seats

6 Unreserved Seats

COMPSCI 189 - LEC 001 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Spring 2023
#29127
COMPSCI 189 123 - DIS 123 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
11:00 am - 11:59 am
Etcheverry 3111
Class #:29127
Units:4

Instruction Mode: In-Person Instruction

Open Seats

35 Unreserved Seats

COMPSCI 189 - DIS 123 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Spring 2023
#29215
COMPSCI 189 118 - DIS 118 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
08:00 pm - 08:59 pm
Wheeler 200
Class #:29215
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 118 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Spring 2023
#29214
COMPSCI 189 117 - DIS 117 offered through Electrical Engineering and Computer Sciences

Introduction to Machine Learning

Jan 17, 2023 - May 05, 2023
Tu
07:00 pm - 07:59 pm
Wheeler 220
Class #:29214
Units:4

Instruction Mode: In-Person Instruction

Open Seats

30 Unreserved Seats

COMPSCI 189 - DIS 117 Introduction to Machine Learning more detail
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
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